To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produce...To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.展开更多
In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results i...In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.展开更多
With the start of the new year,Wen Congxiang,managing director of Ningbo Nuoding,a company specialising in the recycling of end-of-life vehicles,has been constantly on the move.Much of his time is spent coordinating w...With the start of the new year,Wen Congxiang,managing director of Ningbo Nuoding,a company specialising in the recycling of end-of-life vehicles,has been constantly on the move.Much of his time is spent coordinating with vehicle collection firms,electric bicycle manufacturers and recycled materials distributors,as he works to build partnerships focused on the targeted collection and distribution of recycled products.展开更多
Material phase-transition represents a significant phenomenon and mechanism in the context of hypervelocity protection.This study presents a thorough analysis of the phase-transition phenomena induced by shock pressur...Material phase-transition represents a significant phenomenon and mechanism in the context of hypervelocity protection.This study presents a thorough analysis of the phase-transition phenomena induced by shock pressure as the shock wave propagates initially to the rear of the projectile.The shock wave that induces a phase-transition is commonly referred to as a macroscopic phase-transition wave,whereas the interface that separates the distinct phases is referred to as macroscopic phase-boundary.The contact interface between the spherical projectile and the thin plate,characterized by its curved surface,plays a significant role in the nonlinear propagation and evolution of wave systems.The pressure distribution along the central axis of a spherical projectile is derived in accordance with the linear decay law observed for axial pressure.On this basis,a quadratic function is employed to characterize the trend of changes in wave front pressure,thereby facilitating the establishment of a model for wave front pressure distribution.Using the phase-transition pressure criterion for materials,the wave front phase evolution process is derived,and the macroscopic phase-boundary is determined.Based on the geometric propagation model(GPM)and the pressure distribution of the wave front,a phase geometric propagation model(PGPM)is proposed.The phase distribution of a spherical projectile impacting a thin plate is obtained by theoretical methods.The accuracy of the PGPM is subsequently validated through a comparison of its results with those obtained from numerical simulations.展开更多
In recent years,significant breakthroughs have been achieved in the exploration of deep volcanic rocks in the Junggar Basin,highlighting their substantial exploration potential.The complex distribution of volcanic res...In recent years,significant breakthroughs have been achieved in the exploration of deep volcanic rocks in the Junggar Basin,highlighting their substantial exploration potential.The complex distribution of volcanic reservoirs is attributed to the multi-phase tectonic evolution within the basin,with their superior reservoir properties playing a crucial role in natural gas formation.However,due to the combined effects of multi-cyclic volcanic eruptions and tectonic activities,predicting volcanic facies distribution and favorable reservoirs remains highly challenging.This study focuses on the third member of the Jiamuhe Formation in the Zhongguai Uplift.By integrating drilling and petrophysical data with well-seismic analysis techniques,a seismic identification model for volcanic reservoirs has been established.The findings reveal that different facies exhibit distinct seismic response characteristics.Andesite,rhyolite,volcanic breccia,and volcanic clastic rocks show variability in amplitude,frequency,and continuity.Using structural-guided filtering,high-resolution coherence analysis,and 3D body carving techniques,the locations of volcanic craters and eruption centers were successfully identified,further clarifying the distribution patterns of volcanic facies.By combining multi-attribute clustering analysis and seismic attribute extraction,a volcanic facies zone distribution map was generated,and favorable exploration directions for volcanic reservoirs were proposed.The study provides technical guidance for the exploration of deep volcanic oil and gas reservoirs in the Junggar Basin and holds significant application value.展开更多
Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity.This problem becomes more severe when edge participation rate is low,as the information collected from different e...Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity.This problem becomes more severe when edge participation rate is low,as the information collected from different edge devices varies significantly.As a result,communication overhead increases,which further slows down the convergence process.To address this challenge,we propose a simple yet effective federated learning framework that improves consistency among edge devices.The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates.In parallel,a global momentum is applied during model aggregation,enabling faster convergence while preserving personalization.This strategy enables efficient propagation of the estimated global update direction to all participating edge devices and maintains alignment in local training,without introducing extra memory or communication overhead.We conduct extensive experiments on benchmark datasets such as Cifar100 and Tiny-ImageNet.The results confirm the effectiveness of our framework.On CIFAR-100,our method reaches 55%accuracy with 37 fewer rounds and achieves a competitive final accuracy of 65.46%.Even under extreme non-IID scenarios,it delivers significant improvements in both accuracy and communication efficiency.The implementation is publicly available at https://github.com/sjmp525/CollaborativeComputing/tree/FedCCM(accessed on 20 October 2025).展开更多
Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energ...Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.展开更多
With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing r...With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.展开更多
Tajikistan represents a core region of the biodiversity hotspot in Central Asian mountains and has exceptional vascular plant diversity.However,the species diversity of the country faces urgent conservation challenges...Tajikistan represents a core region of the biodiversity hotspot in Central Asian mountains and has exceptional vascular plant diversity.However,the species diversity of the country faces urgent conservation challenges.There has been a lack of a comprehensive and multidimensional assessment to inform strategic conservation planning.Therefore,this study integrated 4 key biodiversity indices including species richness(SR),phylogenetic diversity(PD),threatened species richness(TSR),and endemic species richness(ESR)to map species diversity distribution patterns,identify conservation gaps,and elucidate their effects of climatic factors.This study revealed that species diversity shows a clear trend of decreasing from the western region to the eastern region of Tajikistan.The central–western mountains(specifically the Gissar-Darvasian and Zeravshanian regions)emerge as irreplaceable biodiversity hotspots.However,we found a severe spatial mismatch between these priority areas and the existing protected areas(PAs).Protection coverage for all hotspots was alarmingly low,ranging from 31.00%to 38.00%.Consequently,a critical 64.80%of integrated priority areas fall outside of the current PAs,representing a major conservation gap.This study identified precipitation seasonality and isothermality as the principal drivers,collectively explaining over 50.00%of the diversity variation and suggesting high vulnerability to hydrological shifts.Furthermore,we detected significant geographic sampling bias in the public biodiversity databases,with the most critical hotspot being systematically under-sampled.This study provides a robust scientific basis for conservation action,highlighting the urgent need to strategically expand PAs in the under-protected southwestern region and to mitigate critical sampling gaps through targeted data digitization and field surveys.These measures are indispensable for securing Tajikistan’s unique biodiversity and achieving the Kunming-Montreal Global Biodiversity Framework Target 3(“30×30 Protection”).展开更多
The Haoping 40 m radio telescope at the National Time Service Center,Chinese Academy of Sciences was built in 2014 and is primarily used to observe navigation satellites and pulsars.Since the first successful very lon...The Haoping 40 m radio telescope at the National Time Service Center,Chinese Academy of Sciences was built in 2014 and is primarily used to observe navigation satellites and pulsars.Since the first successful very long baseline interferometry(VLBI)observation of L-band radio source fringes in 2022,ten observations have been made so far.The stations involved in the observations include the Haoping 40 m radio telescope(Haoping),the Tianma 65 m radio telescope(Tianma),the Nanshan 26 m radio telescope(Urumqi),the Guizhou 500 m radio telescope(FAST),the Jilin 13 m radio telescope(Jilin),the Effelsberg 100 m radio telescope(Effelsberg),the Onsala 25 m radio telescope(Onsala),and the Chiang Mai 40 m radio telescope(Chiang Mai).This paper presents details on the specifications of the Haoping 40 m radio telescope,as well as the design of the VLBI experiment,the observation process,and the data processing.We also discuss the analysis of the fringe results involving the Haoping 40 m radio telescope,using Distributed FX Correlator to obtain excellent results.We confirm that the telescope is capable of participating in VLBI observations and performing specific data processing tasks.It can therefore play a greater role in future VLBI observations.展开更多
This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to t...This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.展开更多
Continual learning fault diagnosis(CLFD)has gained growing interest in mechanical systems for its ability to accumulate and transfer knowledge in dynamic fault diagnosis scenarios.However,existing CLFD methods typical...Continual learning fault diagnosis(CLFD)has gained growing interest in mechanical systems for its ability to accumulate and transfer knowledge in dynamic fault diagnosis scenarios.However,existing CLFD methods typically assume balanced task distributions,neglecting the long-tailed nature of real-world fault occurrences,where certain faults dominate while others are rare.Due to the long-tailed distribution among different me-chanical conditions,excessive attention has been focused on the dominant type,leading to performance de-gradation in rarer types.In this paper,decoupling incremental classifier and representation learning(DICRL)is proposed to address the dual challenges of catastrophic forgetting introduced by incremental tasks and the bias in long-tailed CLFD(LT-CLFD).The core innovation lies in the structural decoupling of incremental classifier learning and representation learning.An instance-balanced sampling strategy is employed to learn more dis-criminative deep representations from the exemplars selected by the herding algorithm and new data.Then,the previous classifiers are frozen to prevent damage to representation learning during backward propagation.Cosine normalization classifier with learnable weight scaling is trained using a class-balanced sampling strategy to enhance classification accuracy.Experimental results demonstrate that DICRL outperforms existing continual learning methods across multiple benchmarks,demonstrating superior performance and robustness in both LT-CLFD and conventional CLFD.DICRL effectively tackles both catastrophic forgetting and long-tailed distribution in CLFD,enabling more reliable fault diagnosis in industrial applications.展开更多
Focusing on civil aircraft flap skew detection design,this paper proposes a high-robustness monitoring design methodology to address insufficient monitor robustness that may trigger false alarms and disrupt airline op...Focusing on civil aircraft flap skew detection design,this paper proposes a high-robustness monitoring design methodology to address insufficient monitor robustness that may trigger false alarms and disrupt airline operations.Based on flap skew detection principles and threshold design criteria,the threshold range is defined with upper limit of maximum deformation under aerodynamic load and lower limit of sensor error margin and nominal flight deformation.Since the complex loading conditions of maximum flap differential deformation(max Δλ)during normal flight cannot be theoretically determined,probabilistic methods are employed:Flight test data from hundreds of sorties are analyzed using generalized extreme value distribution.Confidence levels are verified via Kolmogorov-Smirnov(K-S)hypothesis testing.Then probability density function of max Δλis established.The false alarm rate is calculated through cumulative probability values of max Δλat varying thresholds.Boundary conditions for false alarm rate are determined by safety assessment and dispatch reliability analysis.The derived monitoring threshold is verified against finite element analysis predictions and iron bird rig test.The results confirm the methodology’s validity,meeting all design objectives.展开更多
Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in a...Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.展开更多
Climate change disrupts the distribution of species and restructures their richness patterns.The genus of Asian bamboo,Phyllostachys,possesses significant ecological and economic values,and represents the most species...Climate change disrupts the distribution of species and restructures their richness patterns.The genus of Asian bamboo,Phyllostachys,possesses significant ecological and economic values,and represents the most speciesrich genus in the Bambusoideae subfamily.Based on the distribution data of 46 species and 20 environmental variables,we used the MaxEnt model combined with ArcGIS calculations to simulate current and future potential richness distributions under three distinct CO_(2) emission scenarios.The results showed that the MaxEnt model had a good predictive ability,with a mean area under the working characteristic curve(AUC value)of 0.91 for all species.The main environmental variables that impacted the future distribution of most Phyllostachys species were elevation,variations of seasonal precipitation,and mean diurnal range.Phyllostachys species are currently concentrated in southeastern China.Under future climate projections,18 species exhibited significant habitat contraction across three or more future climate scenarios,but suitable habitats for other species will expand.This enhancement is most pronounced under the extreme climate scenario(2090s-SSP585),primarily driven by high species gains contributing to elevated turnover values across scenarios.The center of maximum richness will progressively shift southwestward over time.Predictive modeling of Phyllostachys richness distribution dynamics under climate change enhances our understanding of its biogeography and informs strategic introduction programs to bamboo management and augments China’s carbon sequestration capacity.展开更多
Weak water-drive offshore reservoirs with complex pore architecture and strong permeability heterogeneity present major challenges,including rapid depletion of formation energy,low waterflood efficiency,and significan...Weak water-drive offshore reservoirs with complex pore architecture and strong permeability heterogeneity present major challenges,including rapid depletion of formation energy,low waterflood efficiency,and significant lateral and vertical variability in crude oil properties,all of which contribute to limited recovery.To support more effective field development,alternative strategies and a deeper understanding of pore-scale flow behavior are urgently needed.In this work,CT imaging and digital image processing were used to construct a digital rock model representative of the target reservoir.A pore-scale flow model was then developed,and the Volume of Fluid(VOF)method was applied to simulate and optimize waterflooding schemes aimed at boosting oil recovery.Optimization focused on adjusting injection rates,varying the oil–water viscosity ratio,and implementing a water-alternating-gas(WAG)process.Results show that,for equal injection volumes,higher injection rates cause early water breakthrough through high-permeability pathways,yielding slower gains in recovery.Lowering the oil–water viscosity ratio improves mobility control,suppresses viscous fingering,enlarges sweep volume,and enhances recovery.When CH_(4)becomes fully miscible,it dissolves into the crude oil,lowering viscosity and eliminating interfacial tension,thereby providing greater displacement efficiency than partially miscible injection.Following a switch from water to gas injection,residual oil saturation decreases and becomes more uniformly distributed,indicating that the combined action of water and gas significantly improves both sweep efficiency and microscopic displacement.展开更多
The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is...The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is proposed to reduce communication load in networked control systems by redesigning existing anti-disturbance DAT algorithms and disturbance observers.Furthermore,a fully distributed event-triggering condition is employed to schedule event times for each agent.Simulation results demonstrate that the proposed ETAD-DAT algorithm is able to achieve accurate average tracking of multiple time-varying reference signals despite the presence of external disturbances,while the communication efficiency can be improved obviously.展开更多
To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy s...To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.展开更多
Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and dr...Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.展开更多
The World Journal of Gastroenterology Editorial Board Members are composed of 357 distinguished experts active in the relevant field,distributed in 46 countries/regions,including Italy(66),Japan(53),United States(31),...The World Journal of Gastroenterology Editorial Board Members are composed of 357 distinguished experts active in the relevant field,distributed in 46 countries/regions,including Italy(66),Japan(53),United States(31),China(29),South Korea(16),Spain(15),Australia(13),Greece(12),Brazil(11),Romania(8),Germany(7),India(7),Taiwan(7),United Kingdom(7),Türkiye(7),Hungary(5),Russia(5).展开更多
基金National Natural Science Foundation of China(12125509,11961141003,12275361,U2267205,12175152,12175121)National Key Research and Development Project(2022YFA1602301)Continuous-support Basic Scientific Research Project。
文摘To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.
文摘In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.
文摘With the start of the new year,Wen Congxiang,managing director of Ningbo Nuoding,a company specialising in the recycling of end-of-life vehicles,has been constantly on the move.Much of his time is spent coordinating with vehicle collection firms,electric bicycle manufacturers and recycled materials distributors,as he works to build partnerships focused on the targeted collection and distribution of recycled products.
基金supported by National Natural Science Foundation of China(Nos.12432018,12372346)the Innovative Research Groups of the National Natural Science Foundation of China(No.12221002)National Natural Science Foundation of China(No.12302493)。
文摘Material phase-transition represents a significant phenomenon and mechanism in the context of hypervelocity protection.This study presents a thorough analysis of the phase-transition phenomena induced by shock pressure as the shock wave propagates initially to the rear of the projectile.The shock wave that induces a phase-transition is commonly referred to as a macroscopic phase-transition wave,whereas the interface that separates the distinct phases is referred to as macroscopic phase-boundary.The contact interface between the spherical projectile and the thin plate,characterized by its curved surface,plays a significant role in the nonlinear propagation and evolution of wave systems.The pressure distribution along the central axis of a spherical projectile is derived in accordance with the linear decay law observed for axial pressure.On this basis,a quadratic function is employed to characterize the trend of changes in wave front pressure,thereby facilitating the establishment of a model for wave front pressure distribution.Using the phase-transition pressure criterion for materials,the wave front phase evolution process is derived,and the macroscopic phase-boundary is determined.Based on the geometric propagation model(GPM)and the pressure distribution of the wave front,a phase geometric propagation model(PGPM)is proposed.The phase distribution of a spherical projectile impacting a thin plate is obtained by theoretical methods.The accuracy of the PGPM is subsequently validated through a comparison of its results with those obtained from numerical simulations.
文摘In recent years,significant breakthroughs have been achieved in the exploration of deep volcanic rocks in the Junggar Basin,highlighting their substantial exploration potential.The complex distribution of volcanic reservoirs is attributed to the multi-phase tectonic evolution within the basin,with their superior reservoir properties playing a crucial role in natural gas formation.However,due to the combined effects of multi-cyclic volcanic eruptions and tectonic activities,predicting volcanic facies distribution and favorable reservoirs remains highly challenging.This study focuses on the third member of the Jiamuhe Formation in the Zhongguai Uplift.By integrating drilling and petrophysical data with well-seismic analysis techniques,a seismic identification model for volcanic reservoirs has been established.The findings reveal that different facies exhibit distinct seismic response characteristics.Andesite,rhyolite,volcanic breccia,and volcanic clastic rocks show variability in amplitude,frequency,and continuity.Using structural-guided filtering,high-resolution coherence analysis,and 3D body carving techniques,the locations of volcanic craters and eruption centers were successfully identified,further clarifying the distribution patterns of volcanic facies.By combining multi-attribute clustering analysis and seismic attribute extraction,a volcanic facies zone distribution map was generated,and favorable exploration directions for volcanic reservoirs were proposed.The study provides technical guidance for the exploration of deep volcanic oil and gas reservoirs in the Junggar Basin and holds significant application value.
基金supported by the National Natural Science Foundation of China(62462040)the Yunnan Fundamental Research Projects(202501AT070345)the Major Science and Technology Projects in Yunnan Province(202202AD080013).
文摘Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity.This problem becomes more severe when edge participation rate is low,as the information collected from different edge devices varies significantly.As a result,communication overhead increases,which further slows down the convergence process.To address this challenge,we propose a simple yet effective federated learning framework that improves consistency among edge devices.The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates.In parallel,a global momentum is applied during model aggregation,enabling faster convergence while preserving personalization.This strategy enables efficient propagation of the estimated global update direction to all participating edge devices and maintains alignment in local training,without introducing extra memory or communication overhead.We conduct extensive experiments on benchmark datasets such as Cifar100 and Tiny-ImageNet.The results confirm the effectiveness of our framework.On CIFAR-100,our method reaches 55%accuracy with 37 fewer rounds and achieves a competitive final accuracy of 65.46%.Even under extreme non-IID scenarios,it delivers significant improvements in both accuracy and communication efficiency.The implementation is publicly available at https://github.com/sjmp525/CollaborativeComputing/tree/FedCCM(accessed on 20 October 2025).
基金supported by Integrated Distribution Network Planning and Operational Enhancement Using Flexibility Domains Under Deep Human-Vehicle-Charger-Road-Grid Coupling(U22B20105).
文摘Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.
文摘With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.
基金the Chinese Academy of Sciences Research Center for Ecology and Environment of Central Asia(RCEECA),the construction and joint research for the China-Tajikistan“Belt and Road”Joint Laboratory on Biodiversity Conservation and Sustainable Use(2024YFE0214200)the Shanghai Cooperation Organization Partnership and International Technology Cooperation Plan of Science and Technology Projects(2023E01018,2025E01056)the Chinese Academy of Sciences President’s International Fellowship Initiative(PIFI)(2024VBC0006).
文摘Tajikistan represents a core region of the biodiversity hotspot in Central Asian mountains and has exceptional vascular plant diversity.However,the species diversity of the country faces urgent conservation challenges.There has been a lack of a comprehensive and multidimensional assessment to inform strategic conservation planning.Therefore,this study integrated 4 key biodiversity indices including species richness(SR),phylogenetic diversity(PD),threatened species richness(TSR),and endemic species richness(ESR)to map species diversity distribution patterns,identify conservation gaps,and elucidate their effects of climatic factors.This study revealed that species diversity shows a clear trend of decreasing from the western region to the eastern region of Tajikistan.The central–western mountains(specifically the Gissar-Darvasian and Zeravshanian regions)emerge as irreplaceable biodiversity hotspots.However,we found a severe spatial mismatch between these priority areas and the existing protected areas(PAs).Protection coverage for all hotspots was alarmingly low,ranging from 31.00%to 38.00%.Consequently,a critical 64.80%of integrated priority areas fall outside of the current PAs,representing a major conservation gap.This study identified precipitation seasonality and isothermality as the principal drivers,collectively explaining over 50.00%of the diversity variation and suggesting high vulnerability to hydrological shifts.Furthermore,we detected significant geographic sampling bias in the public biodiversity databases,with the most critical hotspot being systematically under-sampled.This study provides a robust scientific basis for conservation action,highlighting the urgent need to strategically expand PAs in the under-protected southwestern region and to mitigate critical sampling gaps through targeted data digitization and field surveys.These measures are indispensable for securing Tajikistan’s unique biodiversity and achieving the Kunming-Montreal Global Biodiversity Framework Target 3(“30×30 Protection”).
基金supported by the National Science and Technology Major Project(E152KJ1201)the Natural Science Basic Research Program of Shaanxi(2024JC-YBQN-0036)+1 种基金the National Natural Science Foundation of China(42030105 and 11973046)the National SKA Program of China(2020SKA0120200).
文摘The Haoping 40 m radio telescope at the National Time Service Center,Chinese Academy of Sciences was built in 2014 and is primarily used to observe navigation satellites and pulsars.Since the first successful very long baseline interferometry(VLBI)observation of L-band radio source fringes in 2022,ten observations have been made so far.The stations involved in the observations include the Haoping 40 m radio telescope(Haoping),the Tianma 65 m radio telescope(Tianma),the Nanshan 26 m radio telescope(Urumqi),the Guizhou 500 m radio telescope(FAST),the Jilin 13 m radio telescope(Jilin),the Effelsberg 100 m radio telescope(Effelsberg),the Onsala 25 m radio telescope(Onsala),and the Chiang Mai 40 m radio telescope(Chiang Mai).This paper presents details on the specifications of the Haoping 40 m radio telescope,as well as the design of the VLBI experiment,the observation process,and the data processing.We also discuss the analysis of the fringe results involving the Haoping 40 m radio telescope,using Distributed FX Correlator to obtain excellent results.We confirm that the telescope is capable of participating in VLBI observations and performing specific data processing tasks.It can therefore play a greater role in future VLBI observations.
基金supported by the National Key Research and Development Program of China(2025YFE0213100)the National Natural Science Foundation of China(62422315,62573348)+1 种基金the Natural Science Basic Research Program of Shaanxi(2025JC-YBMS-667)the“Shuang Yi Liu”Construction Foundation(25GH02010366)。
文摘This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.
基金Supported by National Natural Science Foundation of China(Grant No.52272440)Suzhou Science Foundation(Grant Nos.SYG202323,ZXL2022027).
文摘Continual learning fault diagnosis(CLFD)has gained growing interest in mechanical systems for its ability to accumulate and transfer knowledge in dynamic fault diagnosis scenarios.However,existing CLFD methods typically assume balanced task distributions,neglecting the long-tailed nature of real-world fault occurrences,where certain faults dominate while others are rare.Due to the long-tailed distribution among different me-chanical conditions,excessive attention has been focused on the dominant type,leading to performance de-gradation in rarer types.In this paper,decoupling incremental classifier and representation learning(DICRL)is proposed to address the dual challenges of catastrophic forgetting introduced by incremental tasks and the bias in long-tailed CLFD(LT-CLFD).The core innovation lies in the structural decoupling of incremental classifier learning and representation learning.An instance-balanced sampling strategy is employed to learn more dis-criminative deep representations from the exemplars selected by the herding algorithm and new data.Then,the previous classifiers are frozen to prevent damage to representation learning during backward propagation.Cosine normalization classifier with learnable weight scaling is trained using a class-balanced sampling strategy to enhance classification accuracy.Experimental results demonstrate that DICRL outperforms existing continual learning methods across multiple benchmarks,demonstrating superior performance and robustness in both LT-CLFD and conventional CLFD.DICRL effectively tackles both catastrophic forgetting and long-tailed distribution in CLFD,enabling more reliable fault diagnosis in industrial applications.
文摘Focusing on civil aircraft flap skew detection design,this paper proposes a high-robustness monitoring design methodology to address insufficient monitor robustness that may trigger false alarms and disrupt airline operations.Based on flap skew detection principles and threshold design criteria,the threshold range is defined with upper limit of maximum deformation under aerodynamic load and lower limit of sensor error margin and nominal flight deformation.Since the complex loading conditions of maximum flap differential deformation(max Δλ)during normal flight cannot be theoretically determined,probabilistic methods are employed:Flight test data from hundreds of sorties are analyzed using generalized extreme value distribution.Confidence levels are verified via Kolmogorov-Smirnov(K-S)hypothesis testing.Then probability density function of max Δλis established.The false alarm rate is calculated through cumulative probability values of max Δλat varying thresholds.Boundary conditions for false alarm rate are determined by safety assessment and dispatch reliability analysis.The derived monitoring threshold is verified against finite element analysis predictions and iron bird rig test.The results confirm the methodology’s validity,meeting all design objectives.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0208)the National Natural Science Foundation of China(Nos.42171148 and 42330512)the Key R&D Project from the Science and Technology Department of Tibet(No.XZ202501ZY0030).
文摘Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.
基金supported by the National Science Foundation of China(32201643)the Key Research Projects of Yibin,research and integrated demonstration and key technologies for smart bamboo industry(YBZD2024-1).
文摘Climate change disrupts the distribution of species and restructures their richness patterns.The genus of Asian bamboo,Phyllostachys,possesses significant ecological and economic values,and represents the most speciesrich genus in the Bambusoideae subfamily.Based on the distribution data of 46 species and 20 environmental variables,we used the MaxEnt model combined with ArcGIS calculations to simulate current and future potential richness distributions under three distinct CO_(2) emission scenarios.The results showed that the MaxEnt model had a good predictive ability,with a mean area under the working characteristic curve(AUC value)of 0.91 for all species.The main environmental variables that impacted the future distribution of most Phyllostachys species were elevation,variations of seasonal precipitation,and mean diurnal range.Phyllostachys species are currently concentrated in southeastern China.Under future climate projections,18 species exhibited significant habitat contraction across three or more future climate scenarios,but suitable habitats for other species will expand.This enhancement is most pronounced under the extreme climate scenario(2090s-SSP585),primarily driven by high species gains contributing to elevated turnover values across scenarios.The center of maximum richness will progressively shift southwestward over time.Predictive modeling of Phyllostachys richness distribution dynamics under climate change enhances our understanding of its biogeography and informs strategic introduction programs to bamboo management and augments China’s carbon sequestration capacity.
基金funded by the Research Project of CNOOC(China)Co.,Ltd.Shanghai Branch,grant number 202417716474Research Project of CNOOC Limited,grant number KJZX-2024-0102.
文摘Weak water-drive offshore reservoirs with complex pore architecture and strong permeability heterogeneity present major challenges,including rapid depletion of formation energy,low waterflood efficiency,and significant lateral and vertical variability in crude oil properties,all of which contribute to limited recovery.To support more effective field development,alternative strategies and a deeper understanding of pore-scale flow behavior are urgently needed.In this work,CT imaging and digital image processing were used to construct a digital rock model representative of the target reservoir.A pore-scale flow model was then developed,and the Volume of Fluid(VOF)method was applied to simulate and optimize waterflooding schemes aimed at boosting oil recovery.Optimization focused on adjusting injection rates,varying the oil–water viscosity ratio,and implementing a water-alternating-gas(WAG)process.Results show that,for equal injection volumes,higher injection rates cause early water breakthrough through high-permeability pathways,yielding slower gains in recovery.Lowering the oil–water viscosity ratio improves mobility control,suppresses viscous fingering,enlarges sweep volume,and enhances recovery.When CH_(4)becomes fully miscible,it dissolves into the crude oil,lowering viscosity and eliminating interfacial tension,thereby providing greater displacement efficiency than partially miscible injection.Following a switch from water to gas injection,residual oil saturation decreases and becomes more uniformly distributed,indicating that the combined action of water and gas significantly improves both sweep efficiency and microscopic displacement.
基金part supported by the National Natural Science Foundation(62203034,62273126,62203035)the Ling-Yan Research and Development Project of Zhejiang Province of China(2023C03185)。
文摘The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is proposed to reduce communication load in networked control systems by redesigning existing anti-disturbance DAT algorithms and disturbance observers.Furthermore,a fully distributed event-triggering condition is employed to schedule event times for each agent.Simulation results demonstrate that the proposed ETAD-DAT algorithm is able to achieve accurate average tracking of multiple time-varying reference signals despite the presence of external disturbances,while the communication efficiency can be improved obviously.
基金supported in part by the Research on Key Technologies for the Development of an Active Balancing Cooperative Control Systemfor Distribution Networks and the National Natural Science Foundation of China under Grant 521532240029,Grant 62303006.
文摘To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.
基金funded by the National Natural Science Foundation of China(42571311).
文摘Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.
文摘The World Journal of Gastroenterology Editorial Board Members are composed of 357 distinguished experts active in the relevant field,distributed in 46 countries/regions,including Italy(66),Japan(53),United States(31),China(29),South Korea(16),Spain(15),Australia(13),Greece(12),Brazil(11),Romania(8),Germany(7),India(7),Taiwan(7),United Kingdom(7),Türkiye(7),Hungary(5),Russia(5).
